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Principles of Machine Learning: Python Edition

About this course

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using Python, and Azure Notebooks.

What you'll learn

After completing this course, you will be familiar with the following concepts and techniques:

  • Data exploration, preparation and cleaning
  • Supervised machine learning techniques
  • Unsupervised machine learning techniques
  • Model performance improvement

Prerequisites

To complete this course successfully, you should have:

  • A basic knowledge of math
  • Some programming experience – Python is preferred.
  • A willingness to learn through self-paced study.

Course Syllabus

  • Introduction to Machine Learning
  • Exploring Data
  • Data Preparation and Cleaning
  • Getting Started with Supervised Learning
  • Improving Model Performance
  • Machine Learning Algorithms
  • Unsupervised Learning

Note: This syllabus is preliminary and subject to change.

Meet the instructors

Graeme Malcolm

Graeme Malcolm

Senior Content Developer

Microsoft Learning Experiences

Graeme has been a trainer, consultant, and author for longer than he cares to remember, specializing in SQL Server and the Microsoft data platform. He is a Microsoft Certified Solutions Expert for the SQL Server Data Platform and Business Intelligence. After years of working with Microsoft as a partner and vendor, he now works in the Microsoft Learning Experiences team as a senior content developer, where he plans and creates content for developers and data professionals who want to get the best out of Microsoft technologies.

Dr. Steve Elston

Dr. Steve Elston

Managing Director

Quantia Analytics, LLC

Steve is a big data geek and data scientist, with over two decades of experience using R and S/SPLUS for predictive analytics and machine learning. He holds a PhD degree in Geophysics from Princeton University, and has led multi-national data science teams across various companies

Cynthia Rudin

Cynthia Rudin

Associate Professor

MIT and Duke

Cynthia leads the Prediction Analysis Lab at MIT, and is associated with the Computer Science and Artificial Intelligence Laboratory and the Sloan School of Management. She holds a PhD in applied and computational mathematics from Princeton University, and was previously, an associate research scientist at the Center for Computational Learning Systems at Columbia U.

Jonathan Sanito

Senior Content Developer

Microsoft

Jonathan works as a content developer and project manager for Microsoft focusing in Data and Analytics online training. He has worked with trainings for developer and IT pro audiences, from Microsoft Dynamics NAV to Windows Active Directory.

Before coming to Microsoft, Jonathan worked as a consultant for a Microsoft partner, implementing Microsoft Dynamics NAV solutions.

  1. Course Number

    DAT275x
  2. Classes Start

  3. Classes End

  4. Estimated Effort

    Total 36 to 48 hours

Associated Programs:

  1. MicroMasters Program:

    AI Developer
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